Performance-Aware Predictive-Model-Based On-Chip Body-Bias Regulation Strategy for an ULP Multi-Core Cluster in 28nm UTBB FD-SOI

TL;DR

This paper presents a real-time, predictive body-bias regulation strategy for ultra-low-power multi-core chips in 28nm UTBB FD-SOI technology, significantly improving energy efficiency and power management under environmental variations.

Contribution

It introduces a novel on-line performance measurement and predictive modeling approach for body-bias control in ULP platforms operating near-threshold.

Findings

Achieves 2x reduction in leakage power.

Improves energy consumption by 15%.

Predictive model error is reduced to 4% after calibration.

Abstract

The performance and reliability of Ultra-Low-Power (ULP) computing platforms are adversely affected by environmental temperature and process variations. Mitigating the effect of these phenomena becomes crucial when these devices operate near-threshold, due to the magnification of process variations and to the strong temperature inversion effect that affects advanced technology nodes in low-voltage corners, which causes huge overhead due to margining for timing closure. Supporting an extended range of reverse and forward body-bias, UTBB FD-SOI technology provides a powerful knob to compensate for such variations. In this work we propose a methodology to maximize energy efficiency at run-time exploiting body biasing on a ULP platform operating near-threshold. The proposed method relies on on-line performance measurements by means of Process Monitoring Blocks (PMBs) coupled with an on-chip low-power body bias generator. We correlate the measurement performed by the PMBs to the maximum achievable frequency of the system, deriving a predictive model able to estimate it with an error of 9.7% at 0.7V. To minimize the effect of process variations we propose a calibration procedure that allows to use a PMB model affected by only the temperature-induced error, which reduces the frequency estimation error by 2.4x (from 9.7% to 4%). We finally propose a controller architecture relying on the derived models to automatically regulate at run-time the body bias voltage. We demonstrate that adjusting the body bias voltage against environmental temperature variations leads up to 2X reduction in the leakage power and a 15% improvement on the global energy consumption when the system operates at 0.7V and 170MHz